Wind farm model aggregation method and device for doubly-fed wind turbine generator and medium

Abstract

The application discloses a wind farm model aggregation method and device for a double-fed wind turbine generator unit and a medium; it relates to the field of wind power generation and solves the problem that wind farm model aggregation cannot guarantee analysis accuracy and reduce the requirement of calculation burden. Based on the structure of a hybrid wind farm, a dynamic mathematical model of a single unit and the whole wind farm is obtained, the contribution of each WTG to the grid current injection is quantified as a dynamic weight coefficient, and the coefficient is updated in real time according to the operating condition, thus breaking through the limitation of traditional methods which only focus on a single type or simplify the dynamic characteristics. Equivalent models are constructed respectively according to the electrical, control mechanical parameters, wind energy capture characteristics of the turbine and impedance distribution characteristics of the line, so that the aggregation model retains the core physical mechanism of the original wind farm, and the detailed modeling of dozens to hundreds of WTGs is replaced by the single unit equivalent model, thus solving the problem of the calculation burden of large-scale wind farm modeling under the premise of ensuring accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power generation, in particular to a wind farm model aggregation method for doubly-fed wind turbine generators, a device and a medium. BACKGROUND

[0002] With the continuous increase of the penetration rate of large-scale wind power in the power system, establishing an accurate wind farm aggregation model has become a key link for formulating power grid expansion plans, carrying out grid connection research and conducting dynamic and steady-state analysis of the power system. Since a wind farm usually contains dozens to hundreds of wind turbine generators (WTGs), directly modeling each wind turbine generator in detail will result in a sharp increase in the computational load of system analysis. Aggregating single models can achieve overall analysis of the wind farm.

[0003] However, the existing wind farm aggregation methods have the following defects: the complete aggregation method assumes that the wind speeds and parameters of all wind turbine generators are the same, and constructs an equivalent model by simply averaging the parameters, which significantly reduces the accuracy when the regional wind speed difference is large or the wind turbine generator parameters are uneven; the regional aggregation method divides the wind farm into multiple regions according to the wind speed, and uses complete aggregation for each region, which improves the accuracy but increases the model complexity, and is not suitable for large-scale wind farms; the semi-aggregation method maintains the mechanical part intact and only aggregates the electrical part, which requires detailed modeling of the mechanical part of all wind turbine generators, resulting in a large computational burden.

[0004] Therefore, it is an urgent technical problem for those skilled in the art to provide an aggregation method that fuses the core physical information of a wind farm and constructs an equivalent aggregation model that can guarantee analysis accuracy and significantly reduce the computational burden. SUMMARY

[0005] The purpose of the present application is to provide a wind farm model aggregation method for doubly-fed wind turbine generators, a device and a medium, which solves the problem that wind farm model aggregation cannot simultaneously guarantee analysis accuracy and reduce computational burden.

[0006] To solve the above technical problems, the present application provides a wind farm model aggregation method for doubly-fed wind turbine generators, comprising:

[0007] Based on the wind farm structure containing doubly-fed induction motors and constant-speed induction motors, and the dynamic characteristics of the wind farm and the wind turbine generator, a dynamic mathematical model of a single wind turbine generator and a wind farm is obtained;

[0008] According to the dynamic mathematical model, the contribution of each wind turbine generator to the grid injection current is quantified as a dynamic weight coefficient in combination with wind speed difference data and parameter difference data of each wind turbine generator; wherein the dynamic weight coefficient is dynamically updated according to the operating condition;

[0009] According to the dynamic weight coefficient, electrical parameters, control parameters and mechanical parameters of each wind turbine are weighted and aggregated to construct an equivalent wind turbine model;

[0010] According to the wind speed distribution characteristic data, an equivalent turbine model is constructed according to the wind energy conversion principle and the dynamic weight coefficient;

[0011] Based on the line impedance distribution difference data and the electric energy transmission mechanism, an equivalent line impedance model is constructed in combination with the dynamic weight coefficient;

[0012] The equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model are coupled in multiple domains to form a wind farm aggregation model.

[0013] As an optional solution, in the wind farm model aggregation method for a doubly-fed wind turbine, dynamic mathematical models of a single wind turbine and a wind farm are obtained based on a wind farm structure containing a doubly-fed induction motor and a constant-speed induction motor, and dynamic characteristics of the wind farm and the wind turbine, including:

[0014] Dynamic mathematical models of an aerodynamic domain, a mechanical domain, an electromagnetic domain and a control domain of a single wind turbine are established, wherein the dynamic mathematical model of the aerodynamic domain is used to describe the conversion relationship between wind energy and mechanical power, the dynamic mathematical model of the mechanical domain is used to describe the speed and torque transmission characteristics, the dynamic mathematical model of the electromagnetic domain is used to describe the dynamic correlation of current, voltage and flux linkage, and the dynamic mathematical model of the control domain is used to represent the power closed-loop control logic;

[0015] According to the parallel topology relationship of multiple wind turbines in the wind farm, each dynamic mathematical model of a single wind turbine is extended to a whole dynamic mathematical model of the wind farm.

[0016] As an optional solution, in the wind farm model aggregation method for a doubly-fed wind turbine, according to the dynamic mathematical model, the contribution of each wind turbine to the grid injected current is quantified as a dynamic weight coefficient in combination with wind speed difference data and parameter difference data of each wind turbine, including:

[0017] Based on the electromagnetic domain dynamic mathematical model in the dynamic mathematical model, three-phase current is converted to a synchronous rotating d-q coordinate system to obtain active and reactive components;

[0018] The d-q coordinate system is rotated to a d'-q' coordinate system so that the q' axis current component of the total injected current is 0, and the d' axis current component is retained;

[0019] The proportion of the d' axis current component of each wind turbine to the total d' axis current component of the wind farm is taken as the dynamic weight coefficient;

[0020] When the wind speed variation amplitude exceeds a preset threshold or the control mode is changed, the dynamic weight coefficient is recalculated and updated.

[0021] As an optional solution, in the wind farm model aggregation method for the doubly-fed wind turbine, the electrical parameters, control parameters and mechanical parameters of each wind turbine are weighted and aggregated according to the dynamic weight coefficient, and an equivalent wind turbine model is constructed, including:

[0022] The electrical parameters of each wind turbine are extracted, including the excitation inductance and the stator resistance.

[0023] The control parameters of each wind turbine are extracted, including the proportional coefficient and integral coefficient of the PI controller.

[0024] The mechanical parameters of each wind turbine are extracted, including the damping coefficient.

[0025] The electrical parameters, control parameters and mechanical parameters are weighted and summed according to the dynamic weight coefficient, respectively, to obtain equivalent electrical parameters, equivalent control parameters and equivalent mechanical parameters.

[0026] An equivalent wind turbine model is constructed based on the equivalent electrical parameters, equivalent control parameters and equivalent mechanical parameters.

[0027] As an optional solution, in the wind farm model aggregation method for the doubly-fed wind turbine, according to the wind speed distribution characteristic data, an equivalent turbine model is constructed according to the wind energy conversion principle and the dynamic weight coefficient, including:

[0028] Real-time wind speed data of different regions of the wind farm and corresponding wind turbine swept area parameters are collected;

[0029] Based on the wind energy conversion principle, the equivalent wind speed is obtained in combination with the dynamic weight coefficient.

[0030] The optimal power coefficient and optimal tip speed ratio of each wind turbine are weighted and aggregated according to the dynamic weight coefficient, to obtain the equivalent optimal power coefficient and equivalent optimal tip speed ratio.

[0031] The equivalent wind speed, the equivalent optimal power coefficient and the equivalent optimal tip speed ratio are integrated to construct an equivalent turbine model.

[0032] As an optional solution, in the wind farm model aggregation method for the doubly-fed wind turbine, an equivalent line impedance model is constructed based on the line impedance distribution difference data and the electric energy transmission mechanism in combination with the dynamic weight coefficient, including:

[0033] The line impedance per unit value of each wind turbine to the grid common node is obtained.

[0034] The equivalent line impedance is obtained by weighting and aggregating each line impedance unit value according to the dynamic weight coefficient.

[0035] An equivalent line impedance model is constructed according to the equivalent line impedance.

[0036] As an optional solution, in the wind farm model aggregation method for the doubly-fed wind turbine, the equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model are coupled in multiple domains to form a wind farm aggregation model, which comprises:

[0037] A mechanical torque transmission link between the equivalent wind turbine model and the equivalent wind generator model is established.

[0038] An electrical connection relationship between the equivalent wind turbine model and the equivalent line impedance model is established.

[0039] Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model are integrated to form a wind farm aggregation model.

[0040] To solve the above technical problems, the application further provides a wind farm model aggregation device for a doubly-fed wind turbine, which comprises:

[0041] A mathematical model construction module is configured to obtain a dynamic mathematical model of a single wind turbine and a wind farm based on a wind farm structure comprising a doubly-fed induction motor and a constant-speed induction motor, and dynamic characteristics of the wind farm and the wind turbine.

[0042] A weight conversion module is configured to quantify the contribution of each wind turbine to the grid injected current as a dynamic weight coefficient according to the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine, wherein the dynamic weight coefficient is dynamically updated according to the operating condition.

[0043] A first aggregation module is configured to weight and aggregate electrical parameters, control parameters and mechanical parameters of each wind turbine according to the dynamic weight coefficient to construct an equivalent wind turbine model.

[0044] A second aggregation module is configured to construct an equivalent turbine model according to wind energy conversion principles and the dynamic weight coefficient based on wind speed distribution characteristic data.

[0045] A third aggregation module is configured to construct an equivalent line impedance model based on line impedance distribution difference data and power transmission mechanism, combined with the dynamic weight coefficient.

[0046] A coupling module is configured to couple the equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model in multiple domains to form a wind farm aggregation model.

[0047] To solve the above technical problems, the application further provides a wind farm model aggregation device for a doubly-fed wind turbine generator unit, comprising:

[0048] a memory for storing a computer program;

[0049] a processor for executing the computer program to implement the steps of the wind farm model aggregation method for a doubly-fed wind turbine generator unit.

[0050] To solve the above technical problems, the application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the wind farm model aggregation method for a doubly-fed wind turbine generator unit.

[0051] The wind farm model aggregation method for a doubly-fed wind turbine generator unit provided by the application is based on the hybrid wind farm structure of doubly-fed induction motors and fixed-speed induction motors, and simultaneously considers the dynamic characteristics of the wind farm and a single WTG, so that the dynamic mathematical model of a single WTG and the whole wind farm is derived, thereby breaking through the limitation of the traditional method which only focuses on a single type or simplifies the dynamic characteristics. By combining the wind speed difference and parameter difference data, the contribution of each WTG to the grid injection current is quantified as a dynamic weight coefficient, and the coefficient is updated in real time according to the operating conditions (such as wind speed change and control mode change), thereby solving the problem that the traditional static weight cannot adapt to the fluctuation of the wind farm operating conditions. Equivalent models are constructed respectively for the electrical, control and mechanical parameters of the wind turbine generator, the wind energy capture characteristics of the turbine and the impedance distribution characteristics of the line, thereby covering the core elements of the whole link of the wind farm from wind energy input to electrical energy output, and avoiding the local simplification defects of the regional aggregation or semi-aggregation method. The three equivalent models are integrated through multi-domain coupling of the mechanical domain, electromagnetic domain and control domain, so that the aggregation model retains the core physical mechanism of the original wind farm, rather than simply superimposes parameters. The single machine equivalent model replaces the detailed modeling of dozens to hundreds of WTGs, and at the same time avoids the multi-region splitting of the regional aggregation method, thereby solving the problem of the calculation burden of large-scale wind farm modeling under the premise of ensuring the accuracy.

[0052] In addition, the application further provides a device and a medium corresponding to the wind farm model aggregation method for a doubly-fed wind turbine generator unit, and the effects are the same as above. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the application, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0054] Figure 1A flow chart of a wind farm model aggregation method for a doubly-fed wind turbine generator set provided by an embodiment of the present application is shown in the figure;

[0055] Figure 2 An equivalent d-q axis current model and a d'-q' axis diagram of a wind farm provided by an embodiment of the present application are shown in the figure;

[0056] Figure 3 A structure diagram of a wind farm model aggregation device for a doubly-fed wind turbine generator set provided by an embodiment of the present application is shown in the figure;

[0057] Figure 4 A structure diagram of another wind farm model aggregation device for a doubly-fed wind turbine generator set provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, any other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0059] The core of the present application is to provide a wind farm model aggregation method, device and medium for a doubly-fed wind turbine generator set.

[0060] In order for those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0061] An embodiment of the present application provides a wind farm model aggregation method for a doubly-fed wind turbine generator set, as shown in the figure, which comprises the following steps: Figure 1

[0062] S11: obtaining a dynamic mathematical model of a single wind turbine generator and a wind farm based on a wind farm structure containing a doubly-fed induction motor and a constant-speed induction motor, and dynamic characteristics of the wind farm and the wind turbine generator;

[0063] S12: quantifying the contribution of each wind turbine generator to the grid injected current as a dynamic weight coefficient according to the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine generator; wherein the dynamic weight coefficient is dynamically updated according to the operating condition;

[0064] S13: weighting and aggregating the electrical parameters, control parameters and mechanical parameters of each wind turbine generator according to the dynamic weight coefficient to construct an equivalent wind turbine generator model;

[0065] S14: constructing an equivalent turbine model according to the wind speed distribution characteristic data and the dynamic weight coefficient according to the principle of wind energy conversion; ​

[0066] S15: Constructing an equivalent line impedance model based on the line impedance distribution difference data and the power transmission mechanism, combined with a dynamic weight coefficient;

[0067] S16: Coupling the equivalent wind turbine model, the equivalent line impedance model, and the equivalent wind farm model to form a wind farm aggregation model.

[0068] In step S11, first, the dynamic mathematical model of a single wind turbine and a wind farm is obtained based on the wind farm structure containing doubly-fed induction generators and fixed-speed induction generators, and the dynamic characteristics of the wind farm and the wind turbine in the embodiment, covering the two common core unit types in the wind farm, i.e., doubly-fed induction generators and fixed-speed induction generators. Since actual wind farms often mix different types of units to adapt to diversified operation requirements, this model construction method can more truly reflect the composition characteristics of the wind farm.

[0069] Secondly, the dynamic mathematical model of a single WTG and the overall dynamic mathematical model of a wind farm are constructed respectively, which not only grasps the physical characteristics of individual units (such as wind energy conversion, electromagnetic response, etc.), but also clearly defines the coupling relationship when multiple units are connected in parallel (such as current superposition, line interaction, etc.). Both of them provide complete physical mechanism support for subsequent aggregation.

[0070] In step S12, according to the dynamic mathematical model, combined with the wind speed difference data and the parameter difference data of each wind turbine, the contribution of each wind turbine to the grid injected current is quantified as a dynamic weight coefficient, and the contribution is quantified according to the actual influence on the grid injected current. Since wind speed differences will cause different unit output powers, and parameter differences will cause different current characteristics (such as phase, amplitude), only the capacity allocation weight cannot reflect the true contribution, and the quantification method based on current contribution is more consistent with the characteristics of the wind farm of the grid.

[0071] In addition, the dynamic weight coefficient is dynamically updated according to the operating conditions. Since the change of operating conditions (such as wind speed fluctuation, control mode switching) will directly change the current output of each unit, if the weight remains unchanged at this time, the aggregation accuracy will inevitably decrease. Based on the above principle, the dynamic updating mechanism ensures that the weight coefficient is always matched with the actual contribution, and it does not mean that the coefficient is fixed or periodically updated, and its updating frequency can be adjusted according to the degree of change of the operating conditions. This embodiment does not make strict restrictions.

[0072] Step S13 aggregates the electrical parameters, control parameters and mechanical parameters of each wind turbine according to the dynamic weight coefficient. The aggregation here is a weighted summation with the dynamic weight as the weight. Since the electrical parameters (such as excitation inductance) affect the electromagnetic response, the control parameters (such as proportional-integral (PI) coefficient) affect the regulation characteristics, and the mechanical parameters (such as damping coefficient) affect the torque transmission, aggregating the three types of parameters respectively can ensure that the equivalent model is consistent with the characteristics of the original wind farm in the electromagnetic, control and mechanical dimensions, and does not mean that only one type of parameter needs to be aggregated.

[0073] Step S14 constructs an equivalent turbine model according to the wind speed distribution characteristic data, the wind energy conversion principle and the dynamic weight coefficient. The wind energy conversion principle determines the nonlinear relationship between wind speed, swept area and captured power, so the construction of the equivalent turbine model needs to follow this physical law, rather than directly weighting the wind speed. The introduction of the dynamic weight ensures that the contribution of wind speed in different regions to the overall wind energy capture is reasonably distributed, and the equivalent model at this time can reflect the overall wind energy input characteristics of the wind farm.

[0074] Step S15 constructs an equivalent line impedance model based on the line impedance distribution difference data and the electric energy transmission mechanism, combined with the dynamic weight coefficient. The electric energy transmission mechanism determines that the line impedance will affect the voltage drop and power loss. Since the actual impact of the line impedance of different units on the power grid is related to their current contribution (dynamic weight coefficient), the aggregation method based on the dynamic weight is more accurate than simple averaging.

[0075] Step S16 multi-domain couples the equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model. The energy flow of the wind farm is a continuous process from wind energy to mechanical energy to electrical energy to the power grid, involving cross-domain interaction in the aerodynamic domain, the mechanical domain and the electromagnetic domain. Therefore, multi-domain coupling is not simply splicing the model, but establishing the mechanical torque transmission between the turbine and the generator (mechanical energy coupling), the electrical connection between the generator and the line (electrical energy coupling), and realizing the coordinated response through the physical laws (such as torque balance and power conservation) in the dynamic mathematical model of the wind farm.

[0076] The wind farm model aggregation method for a doubly-fed wind turbine provided in the application is based on a hybrid wind farm structure of a doubly-fed induction motor and a constant-speed induction motor, and simultaneously considers the dynamic characteristics of the wind farm and a single WTG, and a dynamic mathematical model of a single WTG and the whole wind farm is derived, which breaks through the limitation of traditional methods that only focus on a single type or simplify the dynamic characteristics. By combining wind speed difference and parameter difference data, the contribution of each WTG to the grid injection current is quantified as a dynamic weight coefficient, and the coefficient is updated in real time according to the operating conditions (such as wind speed change and control mode change), which solves the problem that the traditional static weight cannot adapt to the fluctuation of the wind farm operating conditions. The equivalent model is constructed for the electrical, control mechanical parameters, wind energy capture characteristics of the turbine, and impedance distribution characteristics of the line, covering the core elements of the whole link of the wind farm from wind energy input to electric energy output, avoiding the local simplification defects of regional aggregation or semi-aggregation methods. By integrating the three equivalent models through multi-domain coupling of the mechanical domain, electromagnetic domain and control domain, it is ensured that the aggregation model retains the core physical mechanism of the original wind farm, rather than simply superimposing parameters. By replacing the detailed modeling of dozens to hundreds of WTGs with a single unit equivalent model, while avoiding the multi-region splitting of the regional aggregation method, the problem of large-scale wind farm modeling calculation burden is solved under the premise of ensuring accuracy.

[0077] According to the above embodiment, in a further specific embodiment, a dynamic mathematical model of a single wind turbine and a wind farm is obtained based on a wind farm structure comprising a doubly-fed induction motor and a constant-speed induction motor, and dynamic characteristics of the wind farm and the wind turbine, comprising:

[0078] A dynamic mathematical model of the aerodynamic domain, mechanical domain, electromagnetic domain and control domain of a single wind turbine is established, wherein the dynamic mathematical model of the aerodynamic domain is used to describe the conversion relationship between wind energy and mechanical power, the dynamic mathematical model of the mechanical domain is used to describe the speed and torque transmission characteristics, the dynamic mathematical model of the electromagnetic domain is used to describe the dynamic correlation of current, voltage and flux linkage, and the dynamic mathematical model of the control domain is used to represent the power closed-loop control logic;

[0079] According to the parallel topological relationship of a plurality of wind turbines in the wind farm, each dynamic mathematical model of a single wind turbine is extended to a whole dynamic mathematical model of the wind farm.

[0080] In the present embodiment, the step of constructing a dynamic mathematical model based on a hybrid unit structure and dynamic characteristics is refined into two parts of single WTG multi-domain sub-model construction and wind farm whole model extension.

[0081] The purpose of domain modeling is to accurately match the actual physical link of WTG energy conversion. The energy conversion process of WTG is essentially a cross-domain transmission of wind energy to mechanical energy to electric energy, and each link has a unique dynamic law. Therefore, the present embodiment splits the single model into four sub-domains to ensure that each physical process has corresponding mathematical expression support.

[0082] The aerodynamic domain sub-model focuses on the wind energy capture stage, quantifies the influence of wind speed fluctuation on mechanical input by describing the conversion relationship between wind energy and mechanical power (e.g., aerodynamic equation based on tip speed ratio and power coefficient).

[0083] The mechanical domain sub-model takes the mechanical power output of the aerodynamic domain, simulates the dynamic response of the main shaft and gearbox (e.g., speed lag characteristic when wind speed suddenly changes) by describing the speed and torque transmission characteristics (e.g., torque balance equation containing moment of inertia and damping coefficient).

[0084] The electromagnetic domain sub-model targets the core of power generation, accurately reflects the electromagnetic transient characteristics of the WTG (e.g., rotor current fluctuation when grid voltage drops) by describing the dynamic relationship between current, voltage, and flux linkage (e.g., d-q axis differential equation of a doubly-fed induction motor).

[0085] The control domain sub-model takes the power regulation target, simulates the active / reactive power control of the converter (e.g., power frequency regulation when frequency fluctuates) by representing the power closed-loop control logic (e.g., error regulation equation of a PI controller), ensuring that the control characteristics of a single WTG are included in the aggregation.

[0086] The embodiment is based on the parallel topology relationship to expand the overall model, that is, to realize the overall quantification of multi-machine coupling characteristics through topology-level correlation. When expanding, the real parallel topology of the wind farm should be taken as the basis (e.g., each WTG is connected to the common bus through the collection line, and then connected to the grid), the electromagnetic domain sub-model (current, voltage output) of each WTG is correlated with the line impedance model, and the mechanical domain sub-model (speed, torque) is correlated with the wind speed distribution, to ensure the structural authenticity of the overall model and avoid distortion of the coupling characteristics due to topology simplification.

[0087] In the overall model, the output current of the electromagnetic domain sub-model of each single WTG is superimposed at the common bus to form the total injection current of the wind farm; the mechanical domain sub-model of each single WTG is affected by the corresponding regional wind speed to form differentiated mechanical power input, which not only retains the dynamic accuracy of the single model, but also realizes the overall representation of multi-machine coupling characteristics (e.g., when the wind speed in a certain area increases, the corresponding WTG power increases, thereby affecting the total output of the wind farm).

[0088] The local characteristics of single models are integrated into the global characteristics of the wind farm through the topology relationship, while avoiding the huge calculation amount of directly modeling hundreds of WTGs.

[0089] According to the above embodiment, in a further specific embodiment, according to the dynamic mathematical model, the contribution of each wind turbine to the grid injection current is quantified as a dynamic weight coefficient by combining wind speed difference data and parameter difference data of each wind turbine, including:

[0090] Based on the electromagnetic domain dynamic mathematical model in the dynamic mathematical model, the three-phase current is converted to the synchronous rotating d-q coordinate system to obtain the active component and the reactive component;

[0091] Rotating the d-q coordinate system to the d'-q' coordinate system makes the q' axis current component of the total injected current 0, and the d' axis current component is reserved;

[0092] The proportion of the d' axis current component of each wind turbine generator to the total d' axis current component of the wind farm is taken as the dynamic weight coefficient;

[0093] When the wind speed variation amplitude exceeds the preset threshold or the control mode changes, the dynamic weight coefficient is recalculated and updated.

[0094] First, the three-phase current is converted to the d-q coordinate system, which is to convert the alternating current to direct current. Based on the electromagnetic domain submodel in the dynamic mathematical model, the three-phase current of each WTG is converted to the synchronous rotating d-q coordinate system. The essence is to use the commonly used Park transformation in power system analysis to convert the time-varying three-phase alternating current (including fundamental and harmonic components) into a static direct current (d-axis component and q-axis component).

[0095] Among them, the d-axis component corresponds to the active component of the current (directly affects the active power output), and the q-axis component corresponds to the reactive component (mainly affects voltage support). The complex alternating current dynamic analysis is converted into simple direct current analysis, which is convenient for subsequent quantification of the active / reactive contribution of each WTG.

[0096] The rotation of the d-q coordinate system to the d'-q' coordinate system is to eliminate the interference of the reactive power, Figure 2 An equivalent d-q axis current model and a d'-q' axis schematic diagram of a wind farm provided by the embodiments of the present application are shown in Figure 2 The d-q coordinate system is rotated to the d'-q' coordinate system and the q' axis component of the total injected current is 0. By adjusting the rotation angle, the total injected current of all WTGs of the wind farm falls exactly on the new d' axis, and at this time the q' axis component is naturally 0. The core demand of the power grid on the wind farm is stable active power output, and the reactive component more affects the voltage rather than the energy transmission. Therefore, after eliminating the q' axis component, the remaining d' axis current component can purely reflect the actual contribution of each WTG to the total active current, avoiding the contribution misjudgment caused by mixed calculation of active / reactive components in the traditional method.

[0097] The proportion of the d' axis current component of each WTG to the total d' axis current component is taken as the dynamic weight coefficient, and the dynamic weight coefficient of the kth wind turbine generator is ;

[0098] Among them, represents the dynamic weight coefficient of the kth wind turbine generator, represents the current on the d-axis of the kth wind turbine generator, n represents the total number of wind turbine generators in the wind farm. represents the current on the d-axis of the kth wind turbine generator, n represents the total number of wind turbine generators in the wind farm.

[0099] The weight is recalculated when the wind speed changes by more than a preset threshold or the control mode is changed. The wind speed is a core factor affecting the WTG output power, and a sudden change in the wind speed (such as a gust) will cause the d' axis current of each WTG to be redistributed, and a change in the control mode will change the current output characteristics of the WTG, which will also cause the contribution ratio to change. This triggered update can ensure the accuracy of the weight and avoid unnecessary calculation overhead.

[0100] According to the above embodiment, in a further specific embodiment, the electrical parameters, control parameters and mechanical parameters of each wind turbine generator are weighted and aggregated according to the dynamic weight coefficient to construct an equivalent wind turbine generator model, including:

[0101] The electrical parameters of each wind turbine generator are extracted, including the excitation inductance and the stator resistance;

[0102] The control parameters of each wind turbine generator are extracted, including the proportional coefficient and the integral coefficient of the PI controller;

[0103] The mechanical parameters of each wind turbine generator are extracted, including the damping coefficient;

[0104] The electrical parameters, control parameters and mechanical parameters are respectively weighted and summed according to the dynamic weight coefficient to obtain equivalent electrical parameters, equivalent control parameters and equivalent mechanical parameters;

[0105] An equivalent wind turbine generator model is constructed based on the equivalent electrical parameters, equivalent control parameters and equivalent mechanical parameters.

[0106] This embodiment clearly extracts three types of core parameters, electrical parameters, control parameters and mechanical parameters, which directly determine the dynamic response capability of the WTG.

[0107] The electrical parameters (excitation inductance , stator resistance ) are the core determinants of the dynamic characteristics in the electromagnetic domain. The excitation inductance directly affects the flux linkage establishment speed and transient current response of the doubly-fed induction motor; the stator resistance affects the copper loss and voltage drop in the process of power transmission.

[0108] The control parameters (proportional coefficient and integral coefficient of the PI controller) determine the power regulation accuracy and dynamic response speed of the WTG. The larger the power deviation is, the faster the regulation speed is, The larger the static power error is, the smaller the static power error is.

[0109] The mechanical parameter (damping coefficient D) reflects the anti-disturbance ability of the mechanical transmission system. The greater the damping coefficient, the more gentle the response of the rotational speed to the torque fluctuation.

[0110] The three types of parameters are then weighted and summed according to the dynamic weight coefficients, as follows:

[0111] The equivalent electrical parameters include: and ;

[0112] In the formula, represents the equivalent excitation inductance, is the excitation inductance unit value of the kth wind turbine generator, represents the equivalent stator resistance, is the stator resistance unit value of the kth wind turbine generator.

[0113] The equivalent control parameters include: and ;

[0114] In the formula, and are the equivalent proportional coefficient and equivalent integral coefficient, respectively, and are the proportional coefficient and integral coefficient of the controller of the kth wind turbine generator, respectively.

[0115] The equivalent mechanical parameters include: ;

[0116] In the formula, represents the equivalent damping coefficient; is the damping coefficient of the kth wind turbine generator.

[0117] The equivalent wind turbine model is constructed based on the equivalent electrical, control, and mechanical parameters. The core objective is to replace the detailed models of dozens to hundreds of WTGs with a single unit model, reducing the computational burden while ensuring accuracy.

[0118] According to the above embodiments, in further specific embodiments, an equivalent turbine model is constructed according to wind speed distribution characteristic data, based on wind energy conversion principles and dynamic weight coefficients, including:

[0119] Real-time wind speed data and corresponding wind turbine swept area parameters are collected for different regions of the wind farm;

[0120] Based on the wind energy conversion principle, the equivalent wind speed is obtained in combination with the dynamic weight coefficient;

[0121] The optimal power coefficient and optimal tip speed ratio of each wind turbine are weighted and aggregated according to the dynamic weight coefficient to obtain the equivalent optimal power coefficient and equivalent optimal tip speed ratio.

[0122] The equivalent wind speed, the equivalent optimal power coefficient and the equivalent optimal tip speed ratio are integrated to construct an equivalent turbine model.

[0123] In this embodiment, real-time wind speed data of different areas of the wind farm and the swept area parameters of the corresponding WTGs are collected. The core purpose of this data collection design is to restore the spatial difference of wind energy distribution in the wind farm, rather than using the simplified method of average wind speed of the whole farm.

[0124] The core input of wind energy conversion is wind speed, and wind speed has significant regional differences. If the average wind speed is used instead of the real-time wind speed of the region, the differences in wind energy input of different regions will be hidden, and the equivalent model cannot reflect the actual situation that "some areas have high wind speed and some areas have low wind speed".

[0125] The swept area determines the upper limit of the wind energy capture of the WTG (the larger the swept area, the more wind energy is captured under the same wind speed). Therefore, the swept area of the corresponding WTG is collected to combine the wind speed difference with the difference in the capture capacity of the unit and avoid allocating wind energy weight only according to the wind speed. It should be noted that the specific number of area division is not limited in this embodiment, and can be flexibly adjusted according to the actual size of the wind farm and the uniformity of wind speed distribution.

[0126] Based on the principle of wind energy conversion, the equivalent wind speed is obtained by combining the dynamic weight coefficient, and the specific calculation formula is:

[0127] In the formula, represents the equivalent wind speed, represents the wind speed of the kth wind turbine generator, and are the swept area of the kth wind turbine generator and the equivalent swept area of all wind turbine generators, respectively, and .

[0128] The optimal power coefficient and the optimal tip speed ratio of each WTG are respectively weighted and aggregated according to the dynamic weight coefficient, and the purpose is to equivalent the wind energy conversion efficiency characteristics of multiple WTGs to the efficiency characteristics of a single turbine.

[0129] The optimal power coefficient is a core index for measuring the wind energy conversion efficiency of the WTG, which represents the maximum proportion of wind energy converted into mechanical power by the WTG. The optimal tip speed ratio is a key operating parameter for the WTG to realize wind energy conversion.

[0130] Specifically, the optimal power coefficient and the optimal tip speed ratio of each wind turbine generator are respectively weighted and aggregated according to the dynamic weight coefficient to obtain the equivalent optimal power coefficient and the equivalent optimal tip speed ratio, which are calculated by the following formula:

[0131] The equivalent optimal power coefficient is: ​;

[0132] wherein, represents the equivalent optimal power coefficient, is the optimal mechanical power coefficient of the kth wind turbine generator.

[0133] The equivalent optimal tip speed ratio is: ;

[0134] wherein, represents the equivalent optimal tip speed ratio, is the optimal tip speed ratio of the kth wind turbine generator.

[0135] The equivalent wind speed, the equivalent optimal power coefficient and the equivalent optimal tip speed ratio are integrated to construct an equivalent turbine model, and thus the equivalent turbine output mechanical power is: ;

[0136] wherein, represents the equivalent turbine output mechanical power; , and are the equivalent wind energy utilization coefficient and the maximum equivalent wind energy utilization coefficient, respectively, is the air density, is the blade radius of the equivalent turbine, and , is the blade radius of the kth turbine.

[0137] According to the above embodiment, in a further specific embodiment, an equivalent line impedance model is constructed based on line impedance distribution difference data and power transmission mechanism, combined with a dynamic weight coefficient, including:

[0138] Obtaining a line impedance per-unit value of each wind turbine generator to a power grid common node;

[0139] According to the dynamic weight coefficient, the line impedance per-unit values are weighted and aggregated to obtain an equivalent line impedance;

[0140] An equivalent line impedance model is constructed according to the equivalent line impedance.

[0141] This embodiment explicitly obtains a line impedance per-unit value rather than an actual impedance value (such as ohm value), and the core purpose is to eliminate the impedance magnitude difference of different capacity WTGs and different length lines, and to realize unified quantization and comparison.

[0142] The per-unit value is the ratio of the actual value to the reference value, and different physical dimension impedance parameters can be converted into dimensionless relative values. In a wind farm, the line lengths and conductor cross sections of different WTGs to a common node may be different, and the per-unit value can be used to uniformly measure the actual impact on the power grid.

[0143] The power collection system of a wind farm usually adopts a topology structure of multiple wind turbines connected in parallel to a common node, and then connected to the power grid through a main line. The line impedance of each WTG is the sectional impedance from the unit outlet to the common node. The local loss and voltage drop of each WTG in the process of transmitting electric energy to the common node are accurately quantified.

[0144] The equivalent line impedance is obtained by weighting and aggregating the line impedance per unit of each WTG according to the dynamic weight coefficient, and is specifically obtained according to the formula .

[0145] In the formula, , the equivalent line impedance is represented by , and k is the line impedance per unit of the kth wind turbine connected to the power grid.

[0146] The equivalent line impedance model is constructed according to the equivalent line impedance. An equivalent impedance is used to replace the set of line impedances of all WTGs in the wind farm to the common node. The equivalent line impedance model can accurately reproduce the overall voltage drop and loss characteristics of the internal line of the wind farm, and can also meet the simplified calculation requirements of the interaction analysis between large-scale wind farms and power grids.

[0147] According to the above embodiment, in a further specific embodiment, the equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model are coupled in multiple domains to form a wind farm aggregation model, which includes:

[0148] A mechanical torque transmission link between the equivalent turbine model and the equivalent wind turbine model is established.

[0149] An electrical connection relationship between the equivalent wind turbine model and the equivalent line impedance model is established.

[0150] Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model are integrated to form a wind farm aggregation model.

[0151] The multi-domain coupling process of the wind farm aggregation model is refined through the three-step process of mechanical domain coupling, electrical domain coupling and multi-domain logic integration in the specific embodiment.

[0152] The mechanical torque transmission link between the equivalent turbine model and the equivalent wind turbine model is to accurately transmit the mechanical energy converted from wind energy to the generator. The mechanical torque output by the equivalent turbine model (calculated from equivalent wind speed, equivalent power coefficient and other parameters, reflecting the overall mechanical power captured by the wind farm) needs to be the input mechanical torque of the equivalent wind turbine model, which is converted into electric energy through electromagnetic induction of the generator.

[0153] The electrical connection relationship between the equivalent wind power generator model and the equivalent line impedance model is established, and the purpose is to transmit the power output by the generator to the power grid through the line impedance to simulate the loss and voltage drop in the power transmission process. The terminal voltage and current output by the equivalent wind power generator model need to be connected to the equivalent line impedance model, and the influence of the impedance on the power is simulated through the impedance. Finally, the actual output of the wind farm to the power grid is reflected in the form of the voltage and current of the common node. The parameters (equivalent resistance and reactance) of the equivalent line impedance model directly affect the output characteristics of the generator.

[0154] Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the three models are integrated to form the wind farm aggregation model. The interaction of each model is constrained by the unified physical law, and the local coupling is ensured to comply with the global characteristics. The overall dynamic mathematical model of the wind farm has included the cross-domain correlation law of the aerodynamic domain-mechanical domain-electromagnetic domain-control domain (such as torque balance, power conservation, control response), and the three models are integrated based on this as a benchmark, and form a closed loop under the framework of the global mathematical model. Through the global coupling logic, the real-time interaction of the state variables (such as speed, current, and voltage) is realized, and it is ensured that the aggregation model can not only reflect the local details (such as the transient response of the generator), but also reflect the global characteristics (such as the power output curve of the entire wind farm).

[0155] In the above embodiment, the wind farm model aggregation method for a doubly-fed wind power generator set is described in detail, and the present application also provides an embodiment of a wind farm model aggregation device for a doubly-fed wind power generator set. It should be noted that the embodiments of the device part are described from two angles, one is based on the functional module angle, and the other is based on the hardware angle.

[0156] Based on the functional module angle, Figure 3 The structure diagram of a wind farm model aggregation device for a doubly-fed wind power generator set provided by the embodiment of the present application is shown in Figure 3 The wind farm model aggregation device for a doubly-fed wind power generator set includes:

[0157] The mathematical model construction module 21 is configured to obtain the dynamic mathematical model of a single wind power generator and a wind farm based on the wind farm structure containing a doubly-fed induction motor and a constant-speed induction motor, and the dynamic characteristics of the wind farm and the wind power generator.

[0158] The weight conversion module 22 is configured to quantify the contribution of each wind power generator to the grid injected current as a dynamic weight coefficient according to the dynamic mathematical model, in combination with the wind speed difference data and the parameter difference data of each wind power generator. The dynamic weight coefficient is dynamically updated according to the operating conditions.

[0159] The first aggregation module 23 is configured to weight and aggregate the electrical parameters, control parameters and mechanical parameters of each wind power generator according to the dynamic weight coefficient to construct an equivalent wind power generator model.

[0160] The second aggregation module 24 is configured to construct an equivalent turbine model according to the wind speed distribution characteristic data, the wind energy conversion principle and the dynamic weight coefficient;

[0161] The third aggregation module 25 is configured to construct an equivalent line impedance model based on the line impedance distribution difference data, the electric energy transmission mechanism and the dynamic weight coefficient;

[0162] The coupling module 26 is configured to perform multi-domain coupling on the equivalent wind power generator model, the equivalent turbine model and the equivalent line impedance model to form a wind farm aggregation model.

[0163] Since the embodiments of the device part correspond to the embodiments of the method part, the embodiments of the device part are described in the description of the embodiments of the method part, and are not described here.

[0164] Figure 4 Another structural diagram of a wind farm model aggregation device for a doubly-fed wind turbine provided by the embodiments of the present application is shown in FIG. 4. The wind farm model aggregation device for the doubly-fed wind turbine includes a memory 30 configured to store a computer program. Figure 4

[0165] The processor 31 is configured to implement the steps of the method for obtaining the user operation habit information when the computer program is executed.

[0166] The wind farm model aggregation device for the doubly-fed wind turbine provided by the embodiments of the present application can include but is not limited to a mobile terminal, a personal computer, a workstation and the like.

[0167] ​The processor 31 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 31 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 31 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0168] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 30 is used to store at least the following computer program 301, which, after being loaded and executed by the processor 31, is capable of implementing the relevant steps of the wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, and the storage method may be temporary or permanent storage. The operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include, but is not limited to, the data involved in implementing the wind farm model aggregation method for DFIG wind turbines.

[0169] In some embodiments, the wind farm model aggregation device for doubly fed wind turbines may further include a display screen 32, an input / output interface 33, a communication interface 34, a power supply 35, and a communication bus 36.

[0170] Those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on the wind farm model aggregation device for doubly fed wind turbines and may include more or fewer components than shown.

[0171] The wind farm model aggregation device for a doubly-fed wind turbine generator set provided by the embodiment of the present application comprises a memory and a processor, and the processor can realize the following method when executing the program stored in the memory: a wind farm model aggregation method for a doubly-fed wind turbine generator set.

[0172] Finally, the present application also provides an embodiment corresponding to a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps recorded in the above-mentioned embodiment of the wind farm model aggregation method for a doubly-fed wind turbine generator set.

[0173] It can be understood that if the method in the above-mentioned embodiment is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or the whole or part of the technical solution that essentially contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0174] The computer readable storage medium provided by the embodiment of the present application stores a computer program, and when the processor executes the program, the following method can be realized: a wind farm model aggregation method for a doubly-fed wind turbine generator set.

[0175] The wind farm model aggregation method, device and medium for a doubly-fed wind turbine generator set provided by the present application are described in detail above. The embodiments in the specification are described in a progressive manner, and each embodiment mainly describes the differences from other embodiments. The same or similar parts of each embodiment can be referred to. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part. It should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, some improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

[0176] It also needs to be explained that in the present specification, the relational terms such as first and second and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

Claims

1. A wind farm model aggregation method for doubly-fed wind turbines, characterized in that, The method comprises the following steps: Based on the wind farm structure containing doubly-fed induction generators and fixed-speed induction generators, the dynamic characteristics of the wind farm and wind generators, the dynamic mathematical model of a single wind generator and the wind farm is obtained; According to the dynamic mathematical model, combined with the wind speed difference data and the parameter difference data of each wind generator, the contribution of each wind generator to the grid injection current is quantified as a dynamic weight coefficient; wherein the dynamic weight coefficient is dynamically updated according to the operating condition; According to the dynamic weight coefficient, the electrical parameters, control parameters and mechanical parameters of each wind generator are weighted and aggregated to construct an equivalent wind generator model; According to the wind speed distribution characteristic data, the equivalent turbine model is constructed according to the wind energy conversion principle and the dynamic weight coefficient; Based on the line impedance distribution difference data and the electric energy transmission mechanism, combined with the dynamic weight coefficient, an equivalent line impedance model is constructed; The equivalent wind generator model, the equivalent turbine model and the equivalent line impedance model are coupled to form a wind farm aggregation model; The method comprises the following steps: The dynamic mathematical model of a single wind generator is established, including the aerodynamic domain, the mechanical domain, the electromagnetic domain and the control domain. The aerodynamic domain dynamic mathematical model is used to describe the conversion relationship between wind energy and mechanical power. The mechanical domain dynamic mathematical model is used to describe the speed and torque transmission characteristics. The electromagnetic domain dynamic mathematical model is used to describe the dynamic correlation of current, voltage and flux linkage. The control domain dynamic mathematical model is used to represent the power closed-loop control logic. According to the parallel topology relationship of multiple wind generators in the wind farm, the dynamic mathematical model of a single wind generator is extended to the overall dynamic mathematical model of the wind farm. According to the dynamic mathematical model, combined with the wind speed difference data and the parameter difference data of each wind generator, the contribution of each wind generator to the grid injection current is quantified as a dynamic weight coefficient, including: Based on the electromagnetic domain dynamic mathematical model in the dynamic mathematical model, the three-phase current is converted to the synchronous rotating d-q coordinate system to obtain the active and reactive components; The d-q coordinate system is rotated to the d'-q' coordinate system to make the q' axis current component of the total injected current 0, and the d' axis current component is retained; The proportion of the d' axis current component of each wind generator to the total d' axis current component of the wind farm is taken as the dynamic weight coefficient; When the wind speed variation amplitude exceeds the preset threshold or the control mode changes, the dynamic weight coefficient is recalculated and updated; The equivalent wind generator model, the equivalent turbine model and the equivalent line impedance model are coupled to form a wind farm aggregation model, including: The mechanical torque transmission link of the equivalent turbine model and the equivalent wind generator model is established; The electrical connection relationship between the equivalent wind generator model and the equivalent line impedance model is established; Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the equivalent wind generator model, the equivalent turbine model and the equivalent line impedance model are integrated to form a wind farm aggregation model.

2. The doubly-fed wind turbine generator fleet model aggregation method according to claim 1, characterized in that, According to the dynamic weight coefficient, the electrical parameters, control parameters and mechanical parameters of each wind turbine are weighted and aggregated to construct an equivalent wind turbine model, including: Extract the electrical parameters of each wind turbine, including excitation inductance and stator resistance; Extract the control parameters of each wind turbine, including the proportional coefficient and integral coefficient of the PI controller; Extract the mechanical parameters of each wind turbine, including the damping coefficient; According to the dynamic weight coefficient, the electrical parameters, control parameters and mechanical parameters are weighted and summed to obtain equivalent electrical parameters, equivalent control parameters and equivalent mechanical parameters; Based on the equivalent electrical parameters, the equivalent control parameters and the equivalent mechanical parameters, an equivalent wind turbine model is constructed.

3. The doubly-fed wind turbine fleet model aggregation method of claim 2, wherein, According to the wind speed distribution characteristic data, the equivalent turbine model is constructed according to the wind energy conversion principle and the dynamic weight coefficient, including: Collect real-time wind speed data and corresponding wind turbine swept area parameters in different areas of the wind farm; Based on the wind energy conversion principle, the equivalent wind speed is obtained combined with the dynamic weight coefficient; According to the dynamic weight coefficient, the optimal power coefficient and the optimal tip speed ratio of each wind turbine are weighted and aggregated to obtain the equivalent optimal power coefficient and the equivalent optimal tip speed ratio; Integrate the equivalent wind speed, the equivalent optimal power coefficient and the equivalent optimal tip speed ratio to construct an equivalent turbine model.

4. The doubly-fed wind turbine fleet model aggregation method of claim 3, wherein, Based on the line impedance distribution difference data and the electric energy transmission mechanism, the equivalent line impedance model is constructed combined with the dynamic weight coefficient, including: Get the line impedance per unit value of each wind turbine to the grid common node; According to the dynamic weight coefficient, the line impedance per unit value is weighted and aggregated to obtain the equivalent line impedance; According to the equivalent line impedance, an equivalent line impedance model is constructed.

5. A wind farm model aggregation device for doubly-fed wind turbines, characterized by Including: The mathematical model construction module is used to obtain the dynamic mathematical model of single wind turbine and wind farm based on the wind farm structure containing double-fed induction motor and fixed-speed induction motor, and the dynamic characteristics of wind farm and wind turbine; The weight conversion module is used to quantify the contribution of each wind turbine to the grid injected current as a dynamic weight coefficient according to the dynamic mathematical model combined with wind speed difference data and parameter difference data of each wind turbine; wherein the dynamic weight coefficient is dynamically updated with operating conditions; The first aggregation module is used to weight and aggregate the electrical parameters, control parameters and mechanical parameters of each wind turbine according to the dynamic weight coefficient to construct an equivalent wind turbine model; The second aggregation module is used to construct an equivalent turbine model according to the wind speed distribution characteristic data according to the wind energy conversion principle and the dynamic weight coefficient; The third aggregation module is used to construct an equivalent line impedance model based on the line impedance distribution difference data and the electric energy transmission mechanism combined with the dynamic weight coefficient; The coupling module is used to couple the equivalent wind turbine model, the equivalent turbine model and the equivalent line impedance model to form a wind farm aggregation model; Wherein, the dynamic mathematical model of single wind turbine and wind farm is obtained based on the wind farm structure containing double-fed induction motor and fixed-speed induction motor, and the dynamic characteristics of wind farm and wind turbine, including: A dynamic mathematical model of an aerodynamic domain, a mechanical domain, an electromagnetic domain and a control domain of a single wind turbine generator is established, wherein the dynamic mathematical model of the aerodynamic domain is used to describe the conversion relationship between wind energy and mechanical power, the dynamic mathematical model of the mechanical domain is used to describe the speed and torque transmission characteristics, the dynamic mathematical model of the electromagnetic domain is used to describe the dynamic correlation of current, voltage and flux linkage, and the dynamic mathematical model of the control domain is used to represent the power closed-loop control logic; According to the parallel topology relationship of multiple wind turbine generators in a wind farm, the dynamic mathematical model of a single wind turbine generator is extended to a whole dynamic mathematical model of the wind farm; According to the dynamic mathematical model, the contribution of each wind turbine generator to the grid injected current is quantified as a dynamic weight coefficient in combination with wind speed difference data and parameter difference data of each wind turbine generator, including: Based on the electromagnetic domain dynamic mathematical model in the dynamic mathematical model, three-phase current is converted to a synchronous rotating d-q coordinate system to obtain active and reactive components; The d-q coordinate system is rotated to a d'-q' coordinate system so that the q' axis current component of the total injected current is 0, and the d' axis current component is retained; The proportion of the d' axis current component of each wind turbine generator to the total d' axis current component of the wind farm is taken as the dynamic weight coefficient; When the wind speed variation amplitude exceeds a preset threshold or the control mode is changed, the dynamic weight coefficient is recalculated and updated; The equivalent wind turbine generator model, the equivalent turbine model and the equivalent line impedance model are coupled to form a wind farm aggregation model, including: A mechanical torque transmission link between the equivalent turbine model and the equivalent wind turbine generator model is established; An electrical connection relationship between the equivalent wind turbine generator model and the equivalent line impedance model is established; Based on the multi-domain coupling logic of the whole dynamic mathematical model of the wind farm, the equivalent wind turbine generator model, the equivalent turbine model and the equivalent line impedance model are integrated to form a wind farm aggregation model.

6. A wind farm model aggregation device for doubly-fed wind turbines, characterized by It includes: A memory for storing a computer program; A processor for executing the computer program to implement the steps of the wind farm model aggregation method for a doubly-fed wind turbine generator according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the wind farm model aggregation method for a doubly-fed wind turbine generator according to any one of claims 1 to 4.

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